The paper titled "Identifying events in mobility data," by Branko Kavsek, Dunja Mladenic, Omar Malik, and Boleslaw Szymanski was published in Proc. Conference on Data Mining and Data Warehouses (SiKDD 2019) Ljubljana, Slovenia, October 7th, 2019.

The paper titled "Identifying events in mobility data," by Branko Kavsek, Dunja Mladenic, Omar Malik, and Boleslaw Szymanski presented by Dr. Kavsek and published in Proc. Conference on Data Mining and Data Warehouses (SiKDD 2019) Ljubljana, Slovenia, October 7th, 2019. The paper explores interconnectivity of users via our smartphones and access to their phone locations tracked by different apps. ICT technology enables real-time monitoring and processing the user location data from GPS coordinates of a phone. Based on observing the user mobility, Artificial Intelligence methods can be used to improve transportation, proactively provide mobility recommendations and acquire knowledge using the user context. This paper describes the application of machine learning algorithms on user mobility data to identify and understand potentially interesting events. The data for this research was collected from a sample of users consenting to be monitored through our in-house developed smart phone app. A pilot study that includes 227 users that were tracked over a period of 7 years yields fairly positive evaluation results in terms of predictive accuracy of identified events but succeeds in identifying exclusively “well-known” events related to users going to or coming from the office and/or lunch. This shows that machine learning methods can be a suitable choice for identifying events in mobility data but there is still room for improvement.